What is my corpus?

When the course started, we were asked to choose a corpus. Important was to find something that allowed for meaningful comparisons and contrasts, so we could answer a specific research question. This gave me an interesting idea: since 2014, I have been keeping track of all the songs that I have listened to, using a website called Last FM. Every time a track is played, on media players like Spotify or iTunes for example, a “scrobble” is recorded. This way, I have scrobbled a total of 121,587 tracks (and counting!). What better corpus to choose than a corpus that contains a large part of all the music you have ever listened to? Although at the start of this course I had never worked with an API, and had just learned intermediate skills in R in my 3d year of Psychology, it sounded like an interesting challenge. So, I started googling.

Very soon after I found out that this might become a daunting task. Collecting all my scrobbles from the Last FM API wasn’t the hard part; combining over 100,000 songs with Spotify features however, that was something I was not capable of. Luckily, I found a guide written by Andrew Walker, a researcher from the University of Florida, that included detailed instructions on how to do exactly this. Fetching the features would take the longest of the code, he said, likely up to 10-15 minutes. Obviously, for a dataset as large as mine, that was a gross underestimation. When I got the code working, I cut up the fetching process into two parts, my dataset into 5 parts, and let it all run sequentially. 6 hours of long waiting later, it was finally there: all my scrobbles and corresponding Spotify features! From this point on I knew that analyzing my corpus could lead to some very interesting results.

In this portfolio, I will try to answer one main research question: How does time influence my music listening? To answer this question, I will look at three different modes of time: 1. Hour of the day 2. Month of the year 3. Year of my life (also known as age, perhaps)

First, I will provide a visual overview of my data. Here you can find for each year all sorts of interesting descriptive statistics: how much music I’ve listened to, how my Spotify features have developed over time, and more. The most important explanatory variable being of course, time.

Then, I will conduct more detailed analyses. Certain interesting patterns emerged from my preliminary analyses, how can they be explained? Can I find more information about them in chordograms, keygrams, self-similarity matrices?